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Fosca De Iorio; C. Malagelada; Fernando Azpiroz; M. Maluenda; C. Violanti; Laura Igual; Jordi Vitria; Juan R. Malagelada |
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Title |
Intestinal motor activity, endoluminal motion and transit |
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2009 |
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Neurogastroenterology & Motility |
Abbreviated Journal |
NEUMOT |
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21 |
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12 |
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1264–e119 |
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A programme for evaluation of intestinal motility has been recently developed based on endoluminal image analysis using computer vision methodology and machine learning techniques. Our aim was to determine the effect of intestinal muscle inhibition on wall motion, dynamics of luminal content and transit in the small bowel. Fourteen healthy subjects ingested the endoscopic capsule (Pillcam, Given Imaging) in fasting conditions. Seven of them received glucagon (4.8 microg kg(-1) bolus followed by a 9.6 microg kg(-1) h(-1) infusion during 1 h) and in the other seven, fasting activity was recorded, as controls. This dose of glucagon has previously shown to inhibit both tonic and phasic intestinal motor activity. Endoluminal image and displacement was analyzed by means of a computer vision programme specifically developed for the evaluation of muscular activity (contractile and non-contractile patterns), intestinal contents, endoluminal motion and transit. Thirty-minute periods before, during and after glucagon infusion were analyzed and compared with equivalent periods in controls. No differences were found in the parameters measured during the baseline (pretest) periods when comparing glucagon and control experiments. During glucagon infusion, there was a significant reduction in contractile activity (0.2 +/- 0.1 vs 4.2 +/- 0.9 luminal closures per min, P < 0.05; 0.4 +/- 0.1 vs 3.4 +/- 1.2% of images with radial wrinkles, P < 0.05) and a significant reduction of endoluminal motion (82 +/- 9 vs 21 +/- 10% of static images, P < 0.05). Endoluminal image analysis, by means of computer vision and machine learning techniques, can reliably detect reduced intestinal muscle activity and motion. |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ DMA2009 |
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1251 |
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Jordi Vitria; J. Llacer |
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Title |
Reconstructing 3D light microscopic images using the EM algorithm |
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1996 |
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Pattern Recognition Letters |
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17 |
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14 |
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1491–1498 |
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BCNPCL @ bcnpcl @ ViL1996 |
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74 |
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David Guillamet; Jordi Vitria; B. Shiele |
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Introducing a weighted non-negative matrix factorization for image classification |
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2003 |
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Pattern Recognition Letters |
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PRL |
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24 |
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14 |
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2447–2454 |
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IF: 0.809 |
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BCNPCL @ bcnpcl @ GVS2003 |
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382 |
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M. Bressan; Jordi Vitria |
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Title |
Nonparametric Discriminant Analysis and Nearest Neighbor Classification |
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2003 |
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Pattern Recognition Letters |
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PRL |
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24 |
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15 |
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2743–2749 |
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IF: 0.809 |
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BCNPCL @ bcnpcl @ BrV2003b |
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367 |
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Author |
David Masip; Agata Lapedriza; Jordi Vitria |
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Title |
Boosted Online Learning for Face Recognition |
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2009 |
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IEEE Transactions on Systems, Man and Cybernetics part B |
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TSMCB |
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39 |
Issue |
2 |
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530–538 |
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Face recognition applications commonly suffer from three main drawbacks: a reduced training set, information lying in high-dimensional subspaces, and the need to incorporate new people to recognize. In the recent literature, the extension of a face classifier in order to include new people in the model has been solved using online feature extraction techniques. The most successful approaches of those are the extensions of the principal component analysis or the linear discriminant analysis. In the current paper, a new online boosting algorithm is introduced: a face recognition method that extends a boosting-based classifier by adding new classes while avoiding the need of retraining the classifier each time a new person joins the system. The classifier is learned using the multitask learning principle where multiple verification tasks are trained together sharing the same feature space. The new classes are added taking advantage of the structure learned previously, being the addition of new classes not computationally demanding. The present proposal has been (experimentally) validated with two different facial data sets by comparing our approach with the current state-of-the-art techniques. The results show that the proposed online boosting algorithm fares better in terms of final accuracy. In addition, the global performance does not decrease drastically even when the number of classes of the base problem is multiplied by eight. |
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1083–4419 |
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OR;MV |
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BCNPCL @ bcnpcl @ MLV2009 |
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1155 |
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